if(!require("htmltools")) install.packages("htmltools")
if(!require("remotes")) install.packages("remotes")
if(!require("MultiEWCE")) remotes::install_github("neurogenomics/MutltiEWCE", dependencies = TRUE)results <- MultiEWCE::load_example_results()
ctd <- MultiEWCE::load_example_ctd()top_targets <- MultiEWCE::prioritise_targets(results = results,
ctd = ctd)## Prioritising gene targets.## Importing existing file: /Users/schilder/Library/Caches/org.R-project.R/R/HPOExplorer/data/phenotype_to_genes.txt## Adding level- 3 ancestor to each HPO ID.## Prioritised targets:
## - 435,204 results
## - 5,652 phenotypes
## - 77 cell types
## - 0 associated diseases
## - 0 genes## Filtering @ q-value <= 0.05## Prioritised targets:
## - 7,648 results
## - 2,580 phenotypes
## - 77 cell types
## - 0 associated diseases
## - 0 genes## Filtering @ fold-change >= 1## Prioritised targets:
## - 7,648 results
## - 2,580 phenotypes
## - 77 cell types
## - 0 associated diseases
## - 0 genes## Annotating phenos with Tiers.## Prioritised targets:
## - 106 results
## - 11 phenotypes
## - 36 cell types
## - 0 associated diseases
## - 0 genes## Importing existing file: /Users/schilder/Library/Caches/org.R-project.R/R/HPOExplorer/data/phenotype.hpoa## Annotating phenos with Onset.## Importing existing file: /Users/schilder/Library/Caches/org.R-project.R/R/HPOExplorer/data/phenotype.hpoa## Prioritised targets:
## - 523 results
## - 11 phenotypes
## - 36 cell types
## - 42 associated diseases
## - 0 genes## 20 / 36 of cell types kept.## Prioritised targets:
## - 342 results
## - 11 phenotypes
## - 20 cell types
## - 42 associated diseases
## - 0 genes## Filtering by gene size.## Converting phenos to GRanges.## Loading required namespace: ensembldb## Gathering gene metadata## Loading required namespace: EnsDb.Hsapiens.v75## 120 / 2,213 genes kept.## Filtering by specificity_quantile.## Filtering by mean_exp_quantile.## Prioritised targets:
## - 224 results
## - 9 phenotypes
## - 0 cell types
## - 0 associated diseases
## - 70 genes## Prioritised targets:
## - 335 results
## - 9 phenotypes
## - 15 cell types
## - 39 associated diseases
## - 24 genes## Sorting rows.## Prioritised targets:
## - 335 results
## - 9 phenotypes
## - 15 cell types
## - 39 associated diseases
## - 24 genesGenerate a network from the top phenotype-celltype-gene associations.
vn_top <- MultiEWCE::prioritise_targets_network(top_targets = top_targets,
save_path = here::here("reports",
"top_targets_network.html"),
layout = "layout_with_mds",
show_plot = FALSE)## Loading required namespace: visNetwork## Loading required namespace: igraph## Creating network.## Creating plot.## Saving plot ==> /Users/schilder/Desktop/ewce/RareDiseasePrioritisation/reports/top_targets_network.html# visNetwork::renderVisNetwork(vn_top$plot)htmltools::includeHTML("https://github.com/neurogenomics/RareDiseasePrioritisation/raw/master/reports/top_targets_network.html")df_agg <- MultiEWCE::agg_results(phenos = top_targets,
count_var = "CellType",
group_var = "Phenotype")## Aggregating results by group_var='Phenotype'## Adding HPO IDs.## Importing existing file: /Users/schilder/Library/Caches/org.R-project.R/R/HPOExplorer/data/phenotype_to_genes.txtMultiEWCE::create_dt(df_agg)## Loading required namespace: DTSubset phenotypes to those included in intellectual disability, and are related to cognition.
df_intel <- top_targets[disease_characteristic=="Intellectual disability" &
(!Phenotype %in% c("Choreoathetosis","Coma")),]top_genes <- sort(table(df_intel$Gene),
decreasing = TRUE)
print(top_genes)##
## SOX3 SIX6 POU3F4 GSX2 SOX2 FOXG1 PIGY
## 42 41 29 28 28 27 23
## TUBB2A PROP1 GPR88 RTL1 SNORD116-1 SNORD118 FOXH1
## 19 18 14 14 14 14 1
## HOXA2 PRRT2 SLC18A3
## 1 1 1top_celltypes <- sort(table(unique(df_intel[,c("Phenotype","HPO_ID","CellType")])$CellType),
decreasing = TRUE)
print(top_celltypes)##
## Excitatory neurons Ganglion cells Granule neurons Inhibitory neurons
## 4 4 3 3
## Purkinje neurons Amacrine cells Astrocytes ENS glia
## 3 2 2 2
## Horizontal cells Oligodendrocytes Bipolar cells Visceral neurons
## 2 2 1 1Now let’s lift some of the filters on phenotypes and cell types to recover a more extensive network.
all_targets <- MultiEWCE::prioritise_targets(results = results,
ctd = ctd,
# keep_celltypes = NULL,
# keep_onsets = NULL,
keep_tiers = NULL)## Prioritising gene targets.## Importing existing file: /Users/schilder/Library/Caches/org.R-project.R/R/HPOExplorer/data/phenotype_to_genes.txt## Adding level- 3 ancestor to each HPO ID.## Prioritised targets:
## - 435,204 results
## - 5,652 phenotypes
## - 77 cell types
## - 0 associated diseases
## - 0 genes## Filtering @ q-value <= 0.05## Prioritised targets:
## - 7,648 results
## - 2,580 phenotypes
## - 77 cell types
## - 0 associated diseases
## - 0 genes## Filtering @ fold-change >= 1## Prioritised targets:
## - 7,648 results
## - 2,580 phenotypes
## - 77 cell types
## - 0 associated diseases
## - 0 genes## Importing existing file: /Users/schilder/Library/Caches/org.R-project.R/R/HPOExplorer/data/phenotype.hpoa## Annotating phenos with Onset.## Importing existing file: /Users/schilder/Library/Caches/org.R-project.R/R/HPOExplorer/data/phenotype.hpoa## Prioritised targets:
## - 15,558 results
## - 2,563 phenotypes
## - 77 cell types
## - 1,051 associated diseases
## - 0 genes## 37 / 77 of cell types kept.## Prioritised targets:
## - 8,755 results
## - 1,725 phenotypes
## - 37 cell types
## - 937 associated diseases
## - 0 genes## Filtering by gene size.## Converting phenos to GRanges.## Gathering gene metadata## 289 / 4,329 genes kept.## Filtering by specificity_quantile.## Filtering by mean_exp_quantile.## Prioritised targets:
## - 26,832 results
## - 1,438 phenotypes
## - 0 cell types
## - 0 associated diseases
## - 195 genes## Prioritised targets:
## - 9,263 results
## - 1,067 phenotypes
## - 34 cell types
## - 802 associated diseases
## - 111 genes## Sorting rows.## Prioritised targets:
## - 9,263 results
## - 1,067 phenotypes
## - 34 cell types
## - 802 associated diseases
## - 111 genesvn_all <- MultiEWCE::prioritise_targets_network(top_targets = all_targets,
save_path = here::here("reports",
"all_targets_network.html"),
show_plot = FALSE)## Creating network.## Creating plot.## Saving plot ==> /Users/schilder/Desktop/ewce/RareDiseasePrioritisation/reports/all_targets_network.htmlhtmltools::includeHTML("https://github.com/neurogenomics/RareDiseasePrioritisation/raw/master/reports/all_targets_network.html")utils::sessionInfo()## R version 4.2.1 (2022-06-23)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] MultiEWCE_0.1.3 remotes_2.4.2 htmltools_0.5.4
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 R.utils_2.12.2
## [3] tidyselect_1.2.0 RSQLite_2.2.20
## [5] AnnotationDbi_1.60.0 htmlwidgets_1.6.1
## [7] grid_4.2.1 BiocParallel_1.32.5
## [9] munsell_0.5.0 codetools_0.2-18
## [11] DT_0.27 colorspace_2.1-0
## [13] Biobase_2.58.0 filelock_1.0.2
## [15] knitr_1.42 rstudioapi_0.14
## [17] orthogene_1.4.1 stats4_4.2.1
## [19] SingleCellExperiment_1.20.0 ggsignif_0.6.4
## [21] MatrixGenerics_1.10.0 GenomeInfoDbData_1.2.9
## [23] bit64_4.0.5 rprojroot_2.0.3
## [25] coda_0.19-4 vctrs_0.5.2
## [27] treeio_1.22.0 generics_0.1.3
## [29] xfun_0.36 BiocFileCache_2.6.0
## [31] R6_2.5.1 GenomeInfoDb_1.34.6
## [33] pals_1.7 AnnotationFilter_1.22.0
## [35] bitops_1.0-7 cachem_1.0.6
## [37] gridGraphics_0.5-1 DelayedArray_0.24.0
## [39] assertthat_0.2.1 promises_1.2.0.1
## [41] BiocIO_1.8.0 scales_1.2.1
## [43] gtable_0.3.1 ontologyPlot_1.6
## [45] ensembldb_2.22.0 rlang_1.0.6
## [47] rtracklayer_1.58.0 rstatix_0.7.1
## [49] lazyeval_0.2.2 dichromat_2.0-0.1
## [51] broom_1.0.3 BiocManager_1.30.19
## [53] yaml_2.3.7 reshape2_1.4.4
## [55] HPOExplorer_0.99.2 abind_1.4-5
## [57] crosstalk_1.2.0 GenomicFeatures_1.50.4
## [59] ggnetwork_0.5.10 backports_1.4.1
## [61] httpuv_1.6.8 tools_4.2.1
## [63] ggplotify_0.1.0 statnet.common_4.8.0
## [65] ggplot2_3.4.0 ellipsis_0.3.2
## [67] jquerylib_0.1.4 paintmap_1.0
## [69] BiocGenerics_0.44.0 Rcpp_1.0.10
## [71] plyr_1.8.8 visNetwork_2.1.2
## [73] progress_1.2.2 zlibbioc_1.44.0
## [75] purrr_1.0.1 RCurl_1.98-1.9
## [77] prettyunits_1.1.1 ggpubr_0.5.0
## [79] S4Vectors_0.36.1 SummarizedExperiment_1.28.0
## [81] grr_0.9.5 here_1.0.1
## [83] magrittr_2.0.3 data.table_1.14.6
## [85] ProtGenerics_1.30.0 matrixStats_0.63.0
## [87] hms_1.1.2 patchwork_1.1.2
## [89] mime_0.12 evaluate_0.20
## [91] xtable_1.8-4 XML_3.99-0.13
## [93] EWCE_1.6.0 IRanges_2.32.0
## [95] compiler_4.2.1 biomaRt_2.54.0
## [97] tibble_3.1.8 maps_3.4.1
## [99] crayon_1.5.2 R.oo_1.25.0
## [101] ggfun_0.0.9 later_1.3.0
## [103] tidyr_1.3.0 aplot_0.1.9
## [105] DBI_1.1.3 ExperimentHub_2.6.0
## [107] gprofiler2_0.2.1 dbplyr_2.3.0
## [109] rappdirs_0.3.3 babelgene_22.9
## [111] EnsDb.Hsapiens.v75_2.99.0 Matrix_1.5-3
## [113] car_3.1-1 piggyback_0.1.4
## [115] cli_3.6.0 R.methodsS3_1.8.2
## [117] parallel_4.2.1 igraph_1.3.5
## [119] GenomicRanges_1.50.2 pkgconfig_2.0.3
## [121] GenomicAlignments_1.34.0 plotly_4.10.1
## [123] xml2_1.3.3 ggtree_3.6.2
## [125] bslib_0.4.2 XVector_0.38.0
## [127] yulab.utils_0.0.6 stringr_1.5.0
## [129] digest_0.6.31 graph_1.76.0
## [131] Biostrings_2.66.0 rmarkdown_2.20
## [133] HGNChelper_0.8.1 tidytree_0.4.2
## [135] restfulr_0.0.15 curl_5.0.0
## [137] shiny_1.7.4 Rsamtools_2.14.0
## [139] rjson_0.2.21 lifecycle_1.0.3
## [141] nlme_3.1-161 jsonlite_1.8.4
## [143] carData_3.0-5 network_1.18.1
## [145] mapproj_1.2.11 viridisLite_0.4.1
## [147] limma_3.54.0 fansi_1.0.4
## [149] pillar_1.8.1 ontologyIndex_2.10
## [151] lattice_0.20-45 homologene_1.4.68.19.3.27
## [153] KEGGREST_1.38.0 fastmap_1.1.0
## [155] httr_1.4.4 interactiveDisplayBase_1.36.0
## [157] glue_1.6.2 RNOmni_1.0.1
## [159] png_0.1-8 ewceData_1.6.0
## [161] BiocVersion_3.16.0 bit_4.0.5
## [163] Rgraphviz_2.42.0 stringi_1.7.12
## [165] sass_0.4.5 blob_1.2.3
## [167] AnnotationHub_3.6.0 memoise_2.0.1
## [169] dplyr_1.0.10 ape_5.6-2